{"id":"W4400387057","doi":"10.1002/cjs.11810","title":"Estimating the mean squared prediction error of the observed best predictor associated with small area counts: A computationally oriented approach","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Linearization; Mean squared error; Estimation; Computer science; Context (archaeology); Mean squared prediction error; Small area estimation; Statistics; Mathematics; Algorithm; Nonlinear system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005574346,0.0001122552,0.0002043773,0.00006944291,0.0001354967,0.00007374078,0.0002055914,0.00005326651,0.00007850898],"category_scores_gemma":[0.002874856,0.00006271141,0.0000445444,0.000290807,0.0002093416,0.00004143959,0.00000916071,0.0002982517,0.000001227187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001435153,"about_ca_system_score_gemma":0.001242747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001742563,"about_ca_topic_score_gemma":0.001548048,"domain_scores_codex":[0.9988037,0.0001537186,0.0004598898,0.00009604933,0.0003186209,0.0001680446],"domain_scores_gemma":[0.9973685,0.001466833,0.000312811,0.0001258655,0.000573995,0.000151986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003743565,0.0001316734,0.006499744,0.00062791,0.0007521298,0.0001486553,0.004899387,0.001777907,0.00001813711,0.9476123,0.02485391,0.01264082],"study_design_scores_gemma":[0.0005893558,0.0004215649,0.01779039,0.00220593,0.0007441117,0.0001697392,0.0007492566,0.5938354,0.000009366647,0.3825518,0.0007178778,0.0002152516],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005635505,0.00006350676,0.990202,0.0001171208,0.0005300724,0.0001714244,0.002581771,0.000009703053,0.0006889234],"genre_scores_gemma":[0.2357454,0.000001007916,0.7639641,0.00003580055,0.00009127241,0.000004126333,0.00002892478,0.00002260478,0.0001068085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5920574,"threshold_uncertainty_score":0.3441679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1012897990180679,"score_gpt":0.29022013890079,"score_spread":0.1889303398827221,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}